Posts Tagged ‘JMP’
Fractional Factorial Designs with JMP
What Are Fractional Factorial Experiments? In simple terms, a fractional factorial experiment is a subset of a full factorial experiment. Fractional factorials use fewer treatment combinations and runs Fractional factorials are less able to determine effects because of fewer degrees of freedom available to evaluate higher order interactions Fractional factorials can be used to screen…
Read MoreFull Factorial DOE with JMP
Full Factorial DOE In a full factorial experiment, all of the possible combinations of factors and levels are created and tested. For example, for two-level design (i.e.each factor has two levels) with k factors, there are 2k possible scenarios or treatments. Two factors, each with two levels, we have 22 = 4 treatments Three factors, each…
Read MoreLogistic Regression with JMP
What is Logistic Regression? Logistic regression is a statistical method to predict the probability of an event occurring by fitting the data to a logistic curve using logistic function. The regression analysis used for predicting the outcome of a categorical dependent variable, based on one or more predictor variables. The logistic function used to model…
Read MoreStepwise Regression with JMP
What is Stepwise Regression? Stepwise regression is a statistical method to automatically select regression models with the best sets of predictive variables from a large set of potential variables. There are different statistical methods used in stepwise regression to evaluate the potential variables in the model: F-test T-test R-square AIC Three Approaches to Stepwise Regression…
Read MoreMultiple Linear Regression with JMP
What is Multiple Linear Regression? Multiple linear regression is a statistical technique to model the relationship between one dependent variable and two or more independent variables by fitting the data set into a linear equation. The difference between simple linear regression and multiple linear regression: Simple linear regression only has one predictor Multiple linear regression…
Read MoreSimple Linear Regression with JMP
What is Simple Linear Regression? Simple linear regression is a statistical technique to fit a straight line through the data points. It models the quantitative relationship between two variables. It is simple because only one predictor variable is involved. It describes how one variable changes according to the change of another variable. Both variables need…
Read MoreCorrelation Coefficient with JMP
What is Correlation? Correlation is a statistical technique that describes whether and how strongly two or more variables are related. Correlation analysis helps to understand the direction and degree of association between variables, and it suggests whether one variable can be used to predict another. Of the different metrics to measure correlation, Pearson’s correlation coefficient…
Read MoreTwo Sample Proportion Test with JMP
What is the Two Sample Proportion Test? The two sample proportion test is a hypothesis test to compare the proportions of one certain event occurring in two populations following the binomial distribution. Null Hypothesis(H0): p1 = p2 Alternative Hypothesis(Ha): p1 ≠ p2 Two Sample Proportion Test Assumptions The sample data drawn from the populations of…
Read MoreOne Sample t Test with JMP
One Sample t Test What is a t Test? In statistics, a t test is a hypothesis test in which the test statistic follows a Student’s t distribution if the null hypothesis is true. We apply a one sample t test when the population variance (σ) is unknown and we use the sample standard deviation…
Read MoreCentral Limit Theorem with JMP
What is Central Limit Theorem? The Central Limit Theorem is one of the fundamental theorems of probability theory. It states a condition under which the mean of a large number of independent and identically-distributed random variables, each of which has a finite mean and variance, would be approximately normally distributed. Let us assume Y1, Y2…
Read MoreMulti Vari Analysis with JMP
What is Multi-Vari Analysis? Multi-vari analysis is a graphic-driven method to analyze the effects of categorical inputs on a continuous output. It studies how the variation in the output changes across different inputs and helps us quantitatively determine the major source of variability in the output. Multi-vari charts are used to visualize the source of…
Read MoreAttribute MSA with JMP
Use JMP to Implement an Attribute MSA Data File: “AttributeMSA.jmp” Steps in JMP to run an attribute MSA: Click Analyze -> Quality & Process ->Variability/Attribute Gauge Chart Select “Appraiser A”, “Appraiser B” and “Appraiser C” as “Y, Response” Select “Part” as “X,Grouping” Select “Reference” as “Standard” Select “Attribute” as the “Chart Type” Click “OK” Click on…
Read MoreVariable Gage R&R with JMP
What is Variable Gage R&R? Variable Gage Repeatability & Reproducibility (Gage R&R) is a method used to analyze the variability of a measurement system by partitioning the variation of the measurements using ANOVA (Analysis of Variance). Whenever something is measured repeatedly or by different people or processes, the results of the measurements will vary. Variation…
Read MoreHistogram Rendering with JMP
What is a Histogram? A histogram is a graphical tool to present the distribution of the data. The X axis of a histogram represents the possible values of the variable and the Y axis represents the frequency of the value occurring. A histogram consists of adjacent rectangles erected over intervals with heights equal to the…
Read MoreP Chart with JMP
What is a P Chart? The P chart plots the percentage of defectives in one subgroup as a data point. It considers the situation when the subgroup size of inspected units is not constant. The underlying distribution of the P-chart is binomial distribution. P Chart Equations Data Point: Center Line: …
Read MorePareto Analysis with JMP
What is the Pareto Principle? The Pareto principle is an observation not a law. Named after Italian economist Vilfredo Pareto, this principle states that for a variety of situations, 80% of consequences come from 20% of the causes, thus the Pareto principle is also known as the 80/20 rule. The 80/20 rule basically reminds us that…
Read MoreBox Cox Transformation with JMP
What is a Box Cox Transformation? Data transforms are usually applied so that the data appear to more closely meet assumptions of a statistical inference model to be applied or to improve the interpret-ability or appearance of graphs. Power transformation is a class of transformation functions that raise the response to some power. For example, a…
Read MoreBox Plot with JMP
What is a Box Plot? A box plot is a graphical method to summarize a data set by visualizing the minimum value, 25th percentile, median, 75th percentile, the maximum value, and potential outliers. A percentile is the value below which a certain percentage of data fall. For example, if 75% of the observations have values…
Read MoreChi Square Test with JMP
Chi Square (Contingency Tables) We have looked at hypothesis tests to analyze the proportion of one population vs. a specified value, and the proportions of two populations, but what do we do if we want to analyze more than two populations? A chi-square test is a hypothesis test in which the sampling distribution of the…
Read MoreU Chart with JMP
What is a U Chart? The U chart is a type of control chart used to monitor discrete (count) data where the sample size is greater than one, typically the average number of defects per unit. Defect vs. Defective Remember the difference between defect and defective? A defect of a unit is the unit’s characteristic…
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